
"""simple docstring"""



import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline
def    _UpperCAmelCase (					__lowerCamelCase  :			List[str]					,   __lowerCamelCase  :			Tuple						)	->   Tuple:
  _snake_case   	=			args.log_outputs
  _snake_case   	=			'''_'''.join(args.dataset.split('''/'''						) + [args.config, args.split]						)

  # load metric
  _snake_case   	=			load_metric('''wer'''						)
  _snake_case   	=			load_metric('''cer'''						)

  # compute metrics
  _snake_case   	=			wer.compute(references=result['''target''']					,   predictions=result['''prediction''']						)
  _snake_case   	=			cer.compute(references=result['''target''']					,   predictions=result['''prediction''']						)

  # print & log results
  _snake_case   	=			f'''WER: {wer_result}\nCER: {cer_result}'''
  print(lowerCamelCase_						)

  with open(f'''{dataset_id}_eval_results.txt'''					,   '''w'''						) as f:
    f.write(lowerCamelCase_						)

  # log all results in text file. Possibly interesting for analysis
  if log_outputs is not None:
    _snake_case   	=			f'''log_{dataset_id}_predictions.txt'''
    _snake_case   	=			f'''log_{dataset_id}_targets.txt'''

    with open(lowerCamelCase_					,   '''w'''						) as p, open(lowerCamelCase_					,   '''w'''						) as t:
      # mapping function to write output
      def write_to_file(__lowerCamelCase  :			Dict					,   __lowerCamelCase  :			Any						):
        p.write(f'''{i}''' + '''\n'''						)
        p.write(batch['''prediction'''] + '''\n'''						)
        t.write(f'''{i}''' + '''\n'''						)
        t.write(batch['''target'''] + '''\n'''						)

      result.map(lowerCamelCase_					,   with_indices=lowerCamelCase_						)
def    _UpperCAmelCase (					__lowerCamelCase  :			Dict						)	->   Optional[int]:
  _snake_case   	=			'''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

  _snake_case   	=			re.sub(lowerCamelCase_					,   ''''''					,   text.lower()						)

  # In addition, we can normalize the target text, e.g. removing new lines characters etc...
  # note that order is important here!
  _snake_case   	=			['''\n\n''', '''\n''', '''   ''', '''  ''']

  for t in token_sequences_to_ignore:
    _snake_case   	=			''' '''.join(text.split(lowerCamelCase_						)						)

  return text
def    _UpperCAmelCase (					__lowerCamelCase  :			Union[str, Any]						)	->   Union[str, Any]:
  # load dataset
  _snake_case   	=			load_dataset(args.dataset					,   args.config					,   split=args.split					,   use_auth_token=lowerCamelCase_						)

  # for testing: only process the first two examples as a test
  # dataset = dataset.select(range(10))

  # load processor
  _snake_case   	=			AutoFeatureExtractor.from_pretrained(args.model_id						)
  _snake_case   	=			feature_extractor.sampling_rate

  # resample audio
  _snake_case   	=			dataset.cast_column('''audio'''					,   Audio(sampling_rate=lowerCamelCase_						)						)

  # load eval pipeline
  if args.device is None:
    _snake_case   	=			0 if torch.cuda.is_available() else -1
  _snake_case   	=			pipeline('''automatic-speech-recognition'''					,   model=args.model_id					,   device=args.device						)

  # map function to decode audio
  def map_to_pred(__lowerCamelCase  :			Optional[int]						):
    _snake_case   	=			asr(
        batch['''audio''']['''array''']					,   chunk_length_s=args.chunk_length_s					,   stride_length_s=args.stride_length_s						)

    _snake_case   	=			prediction['''text''']
    _snake_case   	=			normalize_text(batch['''sentence''']						)
    return batch

  # run inference on all examples
  _snake_case   	=			dataset.map(lowerCamelCase_					,   remove_columns=dataset.column_names						)

  # compute and log_results
  # do not change function below
  log_results(lowerCamelCase_					,   lowerCamelCase_						)


if __name__ == "__main__":
 UpperCAmelCase__        =					argparse.ArgumentParser()

 parser.add_argument(
     '--model_id', type=str, required=True, help='Model identifier. Should be loadable with 🤗 Transformers'
 )
 parser.add_argument(
     '--dataset',
     type=str,
     required=True,
     help='Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets',
 )
 parser.add_argument(
     '--config', type=str, required=True, help='Config of the dataset. *E.g.* `\'en\'`  for Common Voice'
 )
 parser.add_argument('--split', type=str, required=True, help='Split of the dataset. *E.g.* `\'test\'`')
 parser.add_argument(
     '--chunk_length_s', type=float, default=None, help='Chunk length in seconds. Defaults to 5 seconds.'
 )
 parser.add_argument(
     '--stride_length_s', type=float, default=None, help='Stride of the audio chunks. Defaults to 1 second.'
 )
 parser.add_argument(
     '--log_outputs', action='store_true', help='If defined, write outputs to log file for analysis.'
 )
 parser.add_argument(
     '--device',
     type=int,
     default=None,
     help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.',
 )
 UpperCAmelCase__        =					parser.parse_args()

 main(args)






from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_ = "x"     ,				lowerCamelCase_ = 10**-10     ,				lowerCamelCase_ = 1     ,				):
							A      :					int      		=					symbols(lowerCamelCase_		)
							A      :					List[str]      		=					lambdify(lowerCamelCase_     ,				lowerCamelCase_		)
							A      :					Tuple      		=					lambdify(lowerCamelCase_     ,				diff(lowerCamelCase_     ,				lowerCamelCase_		)		)

							A      :					Optional[int]      		=					starting_point

							while True:
														if diff_function(lowerCamelCase_		) != 0:
																					A      :					Union[str, Any]      		=					prev_guess - multiplicity * func(lowerCamelCase_		) / diff_function(
																					    lowerCamelCase_		)
														else:
																					raise ZeroDivisionError('''Could not find root'''		) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess		) < precision:
																					return next_guess

														A      :					Tuple      		=					next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(F"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")

		# Find root of polynomial
		# Find fourth Root of 5
		print(F"The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}")

		# Find value of e
		print(
		    "The root of log(y) - 1 = 0 is ",
		    F"{newton_raphson('log(y) - 1', 2, variable='y')}",
		)

		# Exponential Roots
		print(
		    "The root of exp(x) - 1 = 0 is",
		    F"{newton_raphson('exp(x) - 1', 10, precision=0.0_05)}",
		)

		# Find root of cos(x)
		print(F"The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}")






